Description of simple genetic algorithm modifications using Generalized Nets

The apparatus of Generalized Nets (GN) is applied here to describe different kinds of genetic algorithms (GA). Failure of conventional optimization methods to lead to a satisfied solution in parameter identification of non-linear and time-dependent parameters provokes an idea some stochastic algorithms to be applied. As such genetic algorithms (GA), as a promising metaheuristic technique, are widely used. Different modifications of simple genetic algorithms (SGA) have been investigated and successfully applied to parameter identification of fermentation processes aiming to improve the model accuracy and the algorithm convergence time. Altogether six modifications of SGA have been proposed with a different sequence of implementation of basic genetic operators selection, crossover and mutation. In the present GN model the user is allowed to choose the sequence of execution of main GA operators, thus resulting in one of the six considered here modifications of SGA.